Loading report..

Highlight Samples

Regex mode off

    Rename Samples

    Click here for bulk input.

    Paste two columns of a tab-delimited table here (eg. from Excel).

    First column should be the old name, second column the new name.

    Regex mode off

      Show / Hide Samples

      Regex mode off

        Export Plots

        px
        px
        X

        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


        Choose Plots

        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        Save Settings

        You can save the toolbox settings for this report to the browser.


        Load Settings

        Choose a saved report profile from the dropdown box below:

        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.13

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/eager analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-01-10, 15:37 UTC based on data in:


        General Statistics

        Showing 83/83 rows and 27/40 columns.
        Sample NameNr. Input ReadsLength Input Reads% GC Input Reads% TrimmedNr. Processed ReadsLength Processed Reads% GC Processed ReadsNr. Reads Into MappingNr. Mapped ReadsEndogenous DNA (%) Post-DeDup Mapped ReadsClusterFactor5 Prime C>T 1st base5 Prime C>T 2nd baseMean Length Mapped ReadsMedian read lengthNr. Dedup. Mapped ReadsMean covMedian cov≥ 1X≥ 2X≥ 3X≥ 4X≥ 5X% GC Dedup. Mapped Reads% Collapsed% Discarded
        55K_A
        19,107,527
        18,362,899
        96.10
        15,630,523
        1.15
        0.7%
        0.6%
        128.22bp
        130.00bp
        55K_A_1
        18,859,450
        151 bp
        51%
        96.9%
        18,761,587
        128 bp
        48%
        91.4%
        4.6%
        55K_A_2
        18,859,450
        151 bp
        51%
        55K_B
        13,025,708
        12,407,783
        95.26
        10,727,239
        1.14
        0.6%
        0.6%
        120.61bp
        122.00bp
        55K_B_1
        13,383,727
        151 bp
        52%
        97.5%
        12,809,198
        120 bp
        49%
        89.3%
        7.5%
        55K_B_2
        13,383,727
        151 bp
        52%
        55K_C
        14,417,317
        13,922,799
        96.57
        11,411,792
        1.18
        0.7%
        0.6%
        133.52bp
        137.00bp
        55K_C_1
        14,036,035
        151 bp
        49%
        95.7%
        14,144,832
        135 bp
        47%
        89.7%
        4.8%
        55K_C_2
        14,036,035
        151 bp
        50%
        55K_udgnone
        37,207,319
        1.9X
        1.0X
        68.8%
        41.7%
        23.7%
        13.2%
        7.5%
        49%
        AL456_A
        40,651,711
        35,906,124
        88.33
        30,942,636
        1.14
        3.2%
        2.6%
        78.11bp
        71.00bp
        AL456_A_1
        40,952,925
        151 bp
        53%
        99.7%
        39,813,450
        77 bp
        50%
        96.7%
        3.1%
        AL456_A_2
        40,952,925
        151 bp
        53%
        AL456_B
        38,328,081
        33,558,809
        87.56
        28,740,464
        1.14
        3.2%
        2.5%
        75.41bp
        68.00bp
        AL456_B_1
        39,356,474
        151 bp
        53%
        99.8%
        37,619,057
        74 bp
        50%
        95.1%
        4.7%
        AL456_B_2
        39,356,474
        151 bp
        55%
        AL456_C
        32,466,459
        28,325,063
        87.24
        24,547,296
        1.13
        3.2%
        2.6%
        74.65bp
        67.00bp
        AL456_C_1
        33,726,155
        151 bp
        55%
        99.7%
        31,883,071
        73 bp
        50%
        94.0%
        5.8%
        AL456_C_2
        33,726,155
        151 bp
        54%
        AL456_udgnone
        82,582,241
        2.5X
        1.0X
        71.0%
        45.0%
        27.2%
        16.8%
        11.0%
        50%
        AL460_A
        42,205,299
        19,420,315
        46.01
        17,054,101
        1.12
        3.1%
        2.4%
        61.55bp
        56.00bp
        AL460_A_1
        41,957,036
        151 bp
        57%
        99.8%
        41,824,735
        57 bp
        49%
        99.3%
        0.5%
        AL460_A_2
        41,957,036
        151 bp
        55%
        AL460_B
        40,974,666
        18,189,786
        44.39
        15,966,218
        1.12
        3.1%
        2.4%
        58.50bp
        53.00bp
        AL460_B_1
        40,930,573
        151 bp
        59%
        99.8%
        40,697,710
        54 bp
        50%
        99.1%
        0.7%
        AL460_B_2
        40,930,573
        151 bp
        57%
        AL460_C
        52,678,871
        23,409,515
        44.44
        20,384,102
        1.13
        3.1%
        2.5%
        59.77bp
        54.00bp
        AL460_C_1
        52,589,758
        151 bp
        59%
        99.8%
        52,273,871
        55 bp
        49%
        99.1%
        0.8%
        AL460_C_2
        52,589,758
        151 bp
        56%
        AL460_udgnone
        52,521,941
        1.2X
        0.0X
        43.6%
        18.9%
        10.3%
        6.8%
        4.9%
        51%
        AL523_A
        45,535,557
        41,728,252
        91.64
        37,270,881
        1.11
        2.4%
        2.2%
        68.27bp
        60.00bp
        AL523_A_1
        43,475,915
        151 bp
        54%
        99.8%
        42,545,199
        68 bp
        50%
        97.4%
        2.4%
        AL523_A_2
        43,475,915
        151 bp
        55%
        AL523_B
        37,876,784
        34,349,139
        90.69
        30,751,113
        1.10
        2.4%
        2.2%
        64.61bp
        57.00bp
        AL523_B_1
        37,029,117
        151 bp
        57%
        99.8%
        35,845,734
        64 bp
        50%
        96.3%
        3.4%
        AL523_B_2
        37,029,117
        151 bp
        55%
        AL523_C
        39,604,986
        35,928,565
        90.72
        32,180,854
        1.10
        2.5%
        2.2%
        66.22bp
        58.00bp
        AL523_C_1
        38,583,073
        151 bp
        56%
        99.8%
        37,256,307
        65 bp
        50%
        96.1%
        3.7%
        AL523_C_2
        38,583,073
        151 bp
        55%
        AL523_udgnone
        93,619,759
        2.4X
        1.0X
        59.6%
        34.8%
        22.6%
        16.6%
        13.3%
        50%
        GL1218_A
        33,115,132
        27,207,840
        82.16
        25,129,447
        1.08
        1.8%
        1.5%
        61.78bp
        54.00bp
        GL1218_A_1
        32,741,042
        151 bp
        56%
        99.8%
        32,791,565
        59 bp
        49%
        99.7%
        0.1%
        GL1218_A_2
        32,741,042
        151 bp
        55%
        GL1218_B
        48,068,270
        38,972,640
        81.08
        35,822,420
        1.08
        1.8%
        1.5%
        59.51bp
        52.00bp
        GL1218_B_1
        47,633,672
        151 bp
        58%
        99.8%
        47,669,472
        57 bp
        49%
        99.7%
        0.1%
        GL1218_B_2
        47,633,672
        151 bp
        55%
        GL1218_C
        53,588,521
        43,276,774
        80.76
        39,744,767
        1.08
        1.7%
        1.5%
        58.94bp
        52.00bp
        GL1218_C_1
        53,118,323
        151 bp
        59%
        99.8%
        53,157,949
        56 bp
        50%
        99.7%
        0.1%
        GL1218_C_2
        53,118,323
        151 bp
        56%
        GL1218_udgnone
        99,739,306
        2.4X
        2.0X
        77.3%
        54.0%
        35.0%
        21.5%
        12.6%
        49%
        GL1221_A
        30,660,491
        25,885,495
        84.43
        23,254,159
        1.10
        1.9%
        1.4%
        64.40bp
        56.00bp
        GL1221_A_1
        29,721,342
        151 bp
        57%
        99.8%
        29,754,230
        62 bp
        52%
        99.7%
        0.1%
        GL1221_A_2
        29,721,342
        151 bp
        56%
        GL1221_B
        48,341,421
        40,452,309
        83.68
        35,874,656
        1.11
        1.9%
        1.4%
        62.72bp
        55.00bp
        GL1221_B_1
        47,009,859
        151 bp
        58%
        99.8%
        47,059,957
        60 bp
        52%
        99.7%
        0.1%
        GL1221_B_2
        47,009,859
        151 bp
        57%
        GL1221_C
        41,260,695
        34,655,733
        83.99
        30,930,328
        1.11
        1.9%
        1.4%
        64.04bp
        56.00bp
        GL1221_C_1
        40,045,647
        151 bp
        57%
        99.8%
        40,091,605
        61 bp
        51%
        99.7%
        0.1%
        GL1221_C_2
        40,045,647
        151 bp
        56%
        GL1221_udgnone
        87,187,572
        2.2X
        1.0X
        65.1%
        40.5%
        25.4%
        16.4%
        11.0%
        52%
        GL1240_A
        27,179,985
        22,938,979
        84.40
        21,135,696
        1.08
        1.5%
        1.2%
        59.58bp
        53.00bp
        GL1240_A_1
        26,646,589
        151 bp
        58%
        99.8%
        26,633,115
        57 bp
        50%
        99.6%
        0.2%
        GL1240_A_2
        26,646,589
        151 bp
        56%
        GL1240_B
        25,709,609
        91,971
        0.36
        22,534
        1.75
        9.3%
        4.8%
        139.85bp
        150.00bp
        GL1240_B_1
        31,425,793
        151 bp
        64%
        99.7%
        25,705,454
        67 bp
        63%
        81.1%
        18.5%
        GL1240_B_2
        31,425,793
        151 bp
        62%
        GL1240_C
        17,370,736
        78,447
        0.45
        19,483
        1.75
        11.0%
        3.9%
        146.16bp
        150.00bp
        GL1240_C_1
        21,118,403
        151 bp
        64%
        99.5%
        17,367,148
        72 bp
        62%
        81.1%
        18.3%
        GL1240_C_2
        21,118,403
        151 bp
        62%
        GL1240_udgnone
        20,693,431
        0.5X
        0.0X
        32.0%
        8.5%
        2.2%
        0.7%
        0.3%
        50%
        GL596
        56,772,052
        50,500,186
        88.95
        45,334,948
        1.10
        2.3%
        2.6%
        66.79bp
        60.00bp
        44,751,265
        1.2X
        1.0X
        58.3%
        29.1%
        12.8%
        5.3%
        2.1%
        48%
        GL596_1
        59,341,736
        151 bp
        55%
        99.8%
        56,078,948
        64 bp
        48%
        94.0%
        5.7%
        GL596_2
        59,341,736
        151 bp
        56%
        RB501_A
        44,308,831
        3,199,377
        7.22
        2,918,791
        1.09
        5.5%
        3.7%
        58.30bp
        52.00bp
        RB501_A_1
        44,248,329
        151 bp
        58%
        99.9%
        44,285,255
        56 bp
        53%
        99.8%
        0.1%
        RB501_A_2
        44,248,329
        151 bp
        57%
        RB501_B
        47,645,285
        39,798,898
        83.53
        34,759,145
        1.13
        1.9%
        1.5%
        65.55bp
        56.00bp
        RB501_B_1
        46,368,279
        151 bp
        58%
        99.7%
        46,202,786
        62 bp
        52%
        99.0%
        0.7%
        RB501_B_2
        46,368,279
        151 bp
        57%
        RB501_C
        37,919,247
        32,183,454
        84.87
        28,207,998
        1.12
        2.0%
        1.5%
        68.00bp
        58.00bp
        RB501_C_1
        36,688,798
        151 bp
        57%
        99.6%
        36,625,808
        65 bp
        51%
        99.0%
        0.6%
        RB501_C_2
        36,688,798
        151 bp
        56%
        RB501_udgnone
        63,575,920
        1.6X
        1.0X
        55.8%
        29.7%
        16.9%
        10.6%
        7.2%
        51%

        FastQC (pre-Trimming)

        FastQC (pre-Trimming) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        loading..

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        loading..

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        loading..

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        loading..

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        loading..

        Sequence Length Distribution

        All samples have sequences of a single length (151bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        loading..

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        loading..

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        loading..

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Adapter Removal

        Adapter Removal rapid adapter trimming, identification, and read merging .DOI: 10.1186/s13104-016-1900-2; 10.1186/1756-0500-5-337.

        Retained and Discarded Reads

        The number of input sequences that were retained, collapsed, and discarded. Be aware that the number of collapsed reads in the output FASTQ will be half of the numbers displayed in this plot, because both R1 and R2 of the collapsed sequences are counted here.

        loading..

        Length Distribution

        The length distribution of reads after processing adapter alignment.

        loading..

        FastQC (post-Trimming)

        FastQC (post-Trimming) is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        loading..

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        loading..

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        loading..

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        loading..

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        loading..

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        loading..

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        loading..

        Overrepresented sequences

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        25 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        loading..

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        loading..

        Samtools Flagstat (pre-samtools filter)

        Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

        Samtools Flagstat

        This module parses the output from samtools flagstat. All numbers in millions.

        loading..

        DeDup

        DeDup is a tool for duplicate removal for merged/collapsed reads in ancient DNA analysis.DOI: 10.1186/s13059-016-0918-z.

        This plot shows read categories that were either not removed (unique reads) or removed (duplicates).

        loading..

        Preseq

        Preseq estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.DOI: 10.1038/nmeth.2375.

        Complexity curve

        Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.

        loading..

        DamageProfiler

        DamageProfiler a tool to determine damage patterns on ancient DNA.DOI: 10.1093/bioinformatics/btab190.

        3P misincorporation plot

        3' misincorporation plot for G>A substitutions

        This plot shows the frequency of G>A substitutions at the 3' read ends. Typically, one would observe high substitution percentages for ancient DNA, whereas modern DNA does not show these in higher extents.

        loading..

        5P misincorporation plot

        5' misincorporation plot for C>T substitutions

        This plot shows the frequency of C>T substitutions at the 5' read ends. Typically, one would observe high substitution percentages for ancient DNA, whereas modern DNA does not show these in higher extents.

        loading..

        Forward read length distribution

        Read length distribution for forward strand (+) reads.

        This plot shows the read length distribution of the forward reads in the investigated sample. Reads below lengths of 30bp are typically filtered, so the plot doesn't show these in many cases. A shifted distribution of read lengths towards smaller read lengths (e.g around 30-50bp) is also an indicator of ancient DNA.

        loading..

        Reverse read length distribution

        Read length distribution for reverse strand (-) reads.

        This plot shows the read length distribution of the reverse reads in the investigated sample. Reads below lengths of 30bp are typically filtered, so the plot doesn't show these in many cases. A shifted distribution of read lengths towards smaller read lengths (e.g around 30-50bp) is also an indicator of ancient DNA.

        loading..

        QualiMap

        QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.DOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503.

        Coverage histogram

        Distribution of the number of locations in the reference genome with a given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

        Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

        If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

        This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

        loading..

        Cumulative genome coverage

        Percentage of the reference genome with at least the given depth of coverage.

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        loading..

        GC content distribution

        Each solid line represents the distribution of GC content of mapped reads for a given sample.

        GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

        QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

        loading..

        nf-core/eager Software Versions

        are collected at run time from the software output.

        nf-core/eager
        v2.4.6
        Nextflow
        v22.10.5
        FastQC
        v0.11.9
        MultiQC
        v1.13
        AdapterRemoval
        v2.3.2
        fastP
        v0.20.1
        BWA
        v0.7.17-r1188
        Bowtie2
        v2.4.4
        circulargenerator
        v1.0
        Samtools
        v1.12
        endorS.py
        v0.4
        DeDup
        v0.12.8
        Picard MarkDuplicates
        v2.26.0
        Qualimap
        v2.2.2-dev
        Preseq
        v3.1.1
        GATK HaplotypeCaller
        v4.2.0.0
        GATK UnifiedGenotyper
        v3.5-0-g36282e4
        freebayes
        v1.3.5
        sequenceTools
        v1.5.2
        VCF2genome
        v0.91
        MTNucRatioCalculator
        v0.7
        bedtools
        v2.30.0
        DamageProfiler
        v0.4.9
        bamUtil
        v1.0.15
        pmdtools
        v0.50
        angsd
        v0.935
        sexdeterrmine
        v1.1.2
        multivcfanalyzer
        v0.85.2
        malt
        v0.6.1
        kraken
        v2.1.2
        maltextract
        v1.7
        eigenstrat_snp_coverage
        v1.0.2
        mapDamage2
        v2.2.1
        bbduk
        v38.92
        bcftools
        v1.12

        nf-core/eager Workflow Summary

        - this information is collected when the pipeline is started.

        Pipeline Release
        master
        Run Name
        stoic_elion
        Input
        input_eager_celebensis2.tsv
        Fasta Ref
        /dss/dssfs03/pn29bi/pn29bi-dss-0002/REF/SUS/Sus_scrofa.Sscrofa11.1.dna.toplevel.fa
        Max Resources
        3 TB memory, 80 cpus, 14d time per job
        Container
        charliecloud - nfcore/eager:2.4.6
        Output dir
        /dss/lxclscratch/05/ra38lur/museum_samples/eager_celebensis4/results/
        Launch dir
        /dss/lxclscratch/05/ra38lur/museum_samples/eager_celebensis4
        Working dir
        /dss/lxclscratch/05/ra38lur/museum_samples/eager_celebensis4/work
        Script dir
        /dss/dsshome1/lxc05/ra38lur/.nextflow/assets/nf-core/eager
        User
        ra38lur
        Config Profile
        charliecloud
        Config Profile Description
        LRZ
        Config Profile Contact
        Rosie Drinkwater
        Config Files
        /dss/dsshome1/lxc05/ra38lur/.nextflow/assets/nf-core/eager/nextflow.config, /dss/lxclscratch/05/ra38lur/museum_samples/eager_celebensis4/lrz_profile_biohpc.config